Package: MultiCOAP 1.1

Wei Liu

MultiCOAP: High-Dimensional Covariate-Augmented Overdispersed Multi-Study Poisson Factor Model

We introduce factor models designed to jointly analyze high-dimensional count data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among counts with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors and the rank of regression coefficient matrix. More details can be referred to Liu et al. (2024) <doi:10.48550/arXiv.2402.15071>.

Authors:Wei Liu [aut, cre], Qingzhi Zhong [aut]

MultiCOAP_1.1.tar.gz
MultiCOAP_1.1.zip(r-4.5)MultiCOAP_1.1.zip(r-4.4)MultiCOAP_1.1.zip(r-4.3)
MultiCOAP_1.1.tgz(r-4.4-x86_64)MultiCOAP_1.1.tgz(r-4.4-arm64)MultiCOAP_1.1.tgz(r-4.3-x86_64)MultiCOAP_1.1.tgz(r-4.3-arm64)
MultiCOAP_1.1.tar.gz(r-4.5-noble)MultiCOAP_1.1.tar.gz(r-4.4-noble)
MultiCOAP_1.1.tgz(r-4.4-emscripten)MultiCOAP_1.1.tgz(r-4.3-emscripten)
MultiCOAP.pdf |MultiCOAP.html
MultiCOAP/json (API)

# Install 'MultiCOAP' in R:
install.packages('MultiCOAP', repos = c('https://feiyoung.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/feiyoung/multicoap/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

3.18 score 1 packages 4 scripts 137 downloads 3 exports 6 dependencies

Last updated 11 days agofrom:0acf705cc5. Checks:OK: 1 WARNING: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-win-x86_64WARNINGOct 26 2024
R-4.5-linux-x86_64WARNINGOct 26 2024
R-4.4-win-x86_64WARNINGOct 26 2024
R-4.4-mac-x86_64WARNINGOct 26 2024
R-4.4-mac-aarch64WARNINGOct 26 2024
R-4.3-win-x86_64WARNINGOct 26 2024
R-4.3-mac-x86_64WARNINGOct 26 2024
R-4.3-mac-aarch64WARNINGOct 26 2024

Exports:gendata_simu_multi2MSFRVIMultiCOAP

Dependencies:irlbalatticeMASSMatrixRcppRcppArmadillo